Automatic target recognition (ATR), which is the ability of a machine to detect and identify objects in a scene and categorizing them into a class, has been the subject of intense research. ATR systems need to deal with uncooperative objects in complex scenes. Statistical fluctuations of the scene and the objects caused by noise, clutter, distortion, rotation, and scaling changes create many challenges toward achieving a reliable ATR system. Numerous techniques using two-dimensional (2D) image processing have been developed while in recent years there has been growing research interest in three-dimensional (3D) object recognition to enhance discrimination capability of unlabeled objects. Additional benefits of 3D imaging include the ability to segment the object of interest from the background and to change the point of view of the observer with respect to the image. Many 3D imaging techniques involve some form of active illumination; the waveform that is transmitted is used to derive the range dimension of the image. However, for imaging applications in which cost and covertness are important, the use of an illumination source may not be feasible.
Photon counting imaging has been applied in many fields such as night vision, laser radar imaging, radiological imaging, and stellar imaging. Advances in the field have produced sensitive receivers that can detect single photons. Devices with high gain produce large charge packet upon absorption of a single incident photon. The number of carriers in charge packet is ignored and only location of the charge packet is recorded.
Therefore, imagery is built up a photo-count at a time, and 2D imagery (irradiance) is developed over time. These highly sensitive receivers allow decrease in required transmitted power over conventional imaging sensors, and trade power for integration time. 2D image recognition using photon counting techniques have been demonstrated. Photon counting techniques have been applied to infrared imaging and thermal imaging. Photon counting detectors have been considered for 3D active sensing by LADAR.
Integral imaging is a sensing and display technique for 3D visualization. An integral imaging system consists of two stages. In the pickup (sensing) stage, an array of microlenses generates a set of 2D elemental images that are captured by a detector array (image sensor). Each elemental image has a different perspective of the 3D object. Therefore, the image sensor records a set of projections of the object from different perspectives. In the reconstruction stage, the recorded 2D elemental images are projected through a similar micro-lens array to produce the original 3D scene. Integral imaging is a passive sensor and unlike holography or LADAR, it does not require active illumination of the scene under investigation. The application of integral imaging has been extended to object recognition and longitudinal distance estimation. The resolution of the 3D image reconstruction and the accuracy of depth estimation have been improved by a moving array lenslet technique. Additionally, statistical pattern recognition technique has been applied for distortion-tolerant 3D object recognition.
In conventional applications, integral imaging has been utilized for the sensing and recognition of unknown targets. Such integral imaging systems have a disadvantage in that they require a large number of photons.
Despite these advances, there is a continuing need in the field for a 3D photon-counting integral imaging system that requires reduced system power and reduced computational time, as well as a need for novel signal processing algorithms that are more efficient than conventional systems in terms of capability for target discrimination.